{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1937dc51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. Area Income</th>\n",
       "      <th>Avg. Area House Age</th>\n",
       "      <th>Avg. Area Number of Rooms</th>\n",
       "      <th>Area Population</th>\n",
       "      <th>size</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79545.45857</td>\n",
       "      <td>5.317139</td>\n",
       "      <td>7.009188</td>\n",
       "      <td>23086.80050</td>\n",
       "      <td>188.214212</td>\n",
       "      <td>1.059034e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79248.64245</td>\n",
       "      <td>4.997100</td>\n",
       "      <td>6.730821</td>\n",
       "      <td>40173.07217</td>\n",
       "      <td>160.042526</td>\n",
       "      <td>1.505891e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61287.06718</td>\n",
       "      <td>5.134110</td>\n",
       "      <td>8.512727</td>\n",
       "      <td>36882.15940</td>\n",
       "      <td>227.273545</td>\n",
       "      <td>1.058988e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63345.24005</td>\n",
       "      <td>3.811764</td>\n",
       "      <td>5.586729</td>\n",
       "      <td>34310.24283</td>\n",
       "      <td>164.816630</td>\n",
       "      <td>1.260617e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59982.19723</td>\n",
       "      <td>5.959445</td>\n",
       "      <td>7.839388</td>\n",
       "      <td>26354.10947</td>\n",
       "      <td>161.966659</td>\n",
       "      <td>6.309435e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
       "0       79545.45857             5.317139                   7.009188   \n",
       "1       79248.64245             4.997100                   6.730821   \n",
       "2       61287.06718             5.134110                   8.512727   \n",
       "3       63345.24005             3.811764                   5.586729   \n",
       "4       59982.19723             5.959445                   7.839388   \n",
       "\n",
       "   Area Population        size         Price  \n",
       "0      23086.80050  188.214212  1.059034e+06  \n",
       "1      40173.07217  160.042526  1.505891e+06  \n",
       "2      36882.15940  227.273545  1.058988e+06  \n",
       "3      34310.24283  164.816630  1.260617e+06  \n",
       "4      26354.10947  161.966659  6.309435e+05  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('usa_housing_price.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1efa25d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "fig1 = plt.subplot(231) #构建一个矩阵，2*3的，这是第一个\n",
    "plt.scatter(data.loc[:,'Avg. Area Income'], data.loc[:, 'Price'])\n",
    "plt.title('price vs income')\n",
    "\n",
    "fig2 = plt.subplot(232) \n",
    "plt.scatter(data.loc[:,'Avg. Area House Age'], data.loc[:, 'Price'])\n",
    "plt.title('price vs age')\n",
    "\n",
    "fig2 = plt.subplot(233) \n",
    "plt.scatter(data.loc[:,'Avg. Area Number of Rooms'], data.loc[:, 'Price'])\n",
    "plt.title('price vs number of rooms')\n",
    "\n",
    "fig2 = plt.subplot(234) \n",
    "plt.scatter(data.loc[:,'Area Population'], data.loc[:, 'Price'])\n",
    "plt.title('price vs area population')\n",
    "\n",
    "fig2 = plt.subplot(235) \n",
    "plt.scatter(data.loc[:,'size'], data.loc[:, 'Price'])\n",
    "plt.title('price vs size')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "836a08b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    188.214212\n",
       "1    160.042526\n",
       "2    227.273545\n",
       "3    164.816630\n",
       "4    161.966659\n",
       "Name: size, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对xy进行赋值\n",
    "x = data.loc[:,'size']\n",
    "y = data.loc[:, 'Price']\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a928410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3674.54995744])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 建立单因子模型\n",
    "from sklearn.linear_model import LinearRegression\n",
    "LR1 = LinearRegression()\n",
    "x = np.array(x).reshape(-1,1)\n",
    "#训练模型\n",
    "LR1.fit(x, y)\n",
    "LR1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3560c024",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1276881.85636623 1173363.58767144 1420407.32457443 ... 1097848.86467426\n",
      " 1264502.88144558 1131278.58816273]\n"
     ]
    }
   ],
   "source": [
    "y_predict_1 = LR1.predict(x)\n",
    "print(y_predict_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6debec68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108771672553.6264 0.1275031240418234\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "mean_squared_error_1 = mean_squared_error(y,y_predict_1)\n",
    "r2_score_1 = r2_score(y,y_predict_1)\n",
    "print(mean_squared_error_1,r2_score_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "286e6541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig6 = plt.figure(figsize=(8,5))\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,y_predict_1,'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e93d0849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. Area Income</th>\n",
       "      <th>Avg. Area House Age</th>\n",
       "      <th>Avg. Area Number of Rooms</th>\n",
       "      <th>Area Population</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79545.45857</td>\n",
       "      <td>5.317139</td>\n",
       "      <td>7.009188</td>\n",
       "      <td>23086.80050</td>\n",
       "      <td>188.214212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79248.64245</td>\n",
       "      <td>4.997100</td>\n",
       "      <td>6.730821</td>\n",
       "      <td>40173.07217</td>\n",
       "      <td>160.042526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61287.06718</td>\n",
       "      <td>5.134110</td>\n",
       "      <td>8.512727</td>\n",
       "      <td>36882.15940</td>\n",
       "      <td>227.273545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63345.24005</td>\n",
       "      <td>3.811764</td>\n",
       "      <td>5.586729</td>\n",
       "      <td>34310.24283</td>\n",
       "      <td>164.816630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59982.19723</td>\n",
       "      <td>5.959445</td>\n",
       "      <td>7.839388</td>\n",
       "      <td>26354.10947</td>\n",
       "      <td>161.966659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>60567.94414</td>\n",
       "      <td>3.169638</td>\n",
       "      <td>6.137356</td>\n",
       "      <td>22837.36103</td>\n",
       "      <td>161.641403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>78491.27543</td>\n",
       "      <td>4.000865</td>\n",
       "      <td>6.576763</td>\n",
       "      <td>25616.11549</td>\n",
       "      <td>159.164596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>63390.68689</td>\n",
       "      <td>3.749409</td>\n",
       "      <td>4.805081</td>\n",
       "      <td>33266.14549</td>\n",
       "      <td>139.491785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>68001.33124</td>\n",
       "      <td>5.465612</td>\n",
       "      <td>7.130144</td>\n",
       "      <td>42625.62016</td>\n",
       "      <td>184.845371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>65510.58180</td>\n",
       "      <td>5.007695</td>\n",
       "      <td>6.792336</td>\n",
       "      <td>46501.28380</td>\n",
       "      <td>148.589423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
       "0          79545.45857             5.317139                   7.009188   \n",
       "1          79248.64245             4.997100                   6.730821   \n",
       "2          61287.06718             5.134110                   8.512727   \n",
       "3          63345.24005             3.811764                   5.586729   \n",
       "4          59982.19723             5.959445                   7.839388   \n",
       "...                ...                  ...                        ...   \n",
       "4995       60567.94414             3.169638                   6.137356   \n",
       "4996       78491.27543             4.000865                   6.576763   \n",
       "4997       63390.68689             3.749409                   4.805081   \n",
       "4998       68001.33124             5.465612                   7.130144   \n",
       "4999       65510.58180             5.007695                   6.792336   \n",
       "\n",
       "      Area Population        size  \n",
       "0         23086.80050  188.214212  \n",
       "1         40173.07217  160.042526  \n",
       "2         36882.15940  227.273545  \n",
       "3         34310.24283  164.816630  \n",
       "4         26354.10947  161.966659  \n",
       "...               ...         ...  \n",
       "4995      22837.36103  161.641403  \n",
       "4996      25616.11549  159.164596  \n",
       "4997      33266.14549  139.491785  \n",
       "4998      42625.62016  184.845371  \n",
       "4999      46501.28380  148.589423  \n",
       "\n",
       "[5000 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#在这里要将price这一列去掉，然后将剩下的几列与price拟合得出结果\n",
    "X_multi = data.drop(['Price'],axis=1)\n",
    "X_multi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "23e915bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set up 2nd linear model\n",
    "LR_multi = LinearRegression()\n",
    "#开始训练模型\n",
    "LR_multi.fit(X_multi,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "61fc1d26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1223968.89166087 1497306.33188629 1250884.31019438 ... 1020693.92390375\n",
      " 1260503.36914585 1302737.79157629]\n"
     ]
    }
   ],
   "source": [
    "#针对训练出来的模型做运算，算出给出条件对应的预测值，并在下方进行代价估计\n",
    "y_predict_multi = LR_multi.predict(X_multi)\n",
    "print(y_predict_multi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d3bcc764",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10219846512.17786 0.9180229195220739\n"
     ]
    }
   ],
   "source": [
    "mean_squared_error_multi = mean_squared_error(y,y_predict_multi)\n",
    "r2_score_multi = r2_score(y,y_predict_multi)\n",
    "print(mean_squared_error_multi,r2_score_multi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9d752196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig7 = plt.figure(figsize=(8,5))\n",
    "plt.scatter(y,y_predict_multi)\n",
    "plt.show()"
   ]
  },
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